Controlling a gps receiver by detecting motion based on radiofrequency signal traces

ABSTRACT

Operation of a GPS receiver on a wireless communications device is controlled by determining whether the device is stationary or in motion. Motion determination is accomplished by analyzing radiofrequency signal traces, e.g. GSM signal traces, received from one or more nearby base stations. A three-tiered analysis provides a progressively more accurate determination as to whether the device is moving or stationary while providing, in certain instances, a more rapid determination than prior-art techniques. When the device is determined to be stationary, the GPS receiver can be deactivated. When the device is determined to be moving, the GPS receiver can be reactivated.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. §119(e)from U.S. Provisional Patent Application Ser. No. 61/221,348 which wasfiled Jun. 29, 2009 and entitled CONTROLLING A GPS RECEIVER BY DETECTINGMOTION BASED ON RADIOFREQUENCY SIGNAL TRACES.

TECHNICAL FIELD

The present disclosure relates generally to GPS-enabled wirelesscommunications devices and, in particular, to techniques for efficientlyoperating an onboard GPS chip to prolong battery life.

BACKGROUND

Wireless communications devices such as the BlackBerry® by Research InMotion Limited provide a variety of useful functions, such as voicecommunication, e-mail and Web browsing. Of growing popularity aremapping and navigation applications or other location-based servicesthat take advantage of a location-fixing system such as a GlobalPositioning System (GPS) receiver, either embedded as a GPS chipset orexternally connected to the device (e.g. via Bluetooth®).

However, the GPS receiver draws a substantial amount of current, thusdiminishing the battery life of the wireless communications device.Accordingly, a technique to selectively disable the GPS chip in order toprolong battery life is highly desirable.

One such technique is proposed by Deblauwe et al. in a publicationentitled “Hybrid GPS and GSM localization—energy-efficient detection ofspatial triggers” published in Positioning, Navigation andCommunication, 2008. WPNC 2008, 27-27 Mar. 2008 pp. 181-189. Thistechnique compares the wireless device's current location as determinedbased on GSM measurements with the last position fix obtained by the GPSchip. This technique can be used to reactivate the GPS chip if thepositions differ. However, there remains a need for a technique that canbe used to both reactivate the GPS chip and also to deactivate the GPSchip.

Accordingly, an improved technique to selectively disable the GPS chipin order to prolong battery life remains highly desirable.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features and advantages of the present technology will becomeapparent from the following detailed description, taken in combinationwith the appended drawings, in which:

FIG. 1 is a schematic depiction of one example of a wirelesscommunications device on which the present technology can beimplemented, the schematic depiction presenting certain main componentsof the device in block diagram format;

FIG. 2 is a schematic depiction of a wireless communications deviceshowing a GPS visual indicator that, in this particular figure,indicates that the GPS chip is operating;

FIG. 3 is a schematic depiction of a wireless communications deviceshowing a GPS visual indicator that, in this particular figure,indicates that the GPS chip is inactive;

FIG. 4 is a schematic depiction of a wireless communications devicereceiving radiofrequency signals from multiple base stations;

FIG. 5 is a flowchart depicting main steps of a method of controllingoperation of a GPS receiver in accordance with an implementation of thepresent technology; and

FIG. 6 is a flowchart depicting main steps of a method of trainingclassifiers for implementing the method depicted in FIG. 5.

It will be noted that throughout the appended drawings, like featuresare identified by like reference numerals.

DETAILED DESCRIPTION

The present technology prolongs battery life on a GPS-equipped wirelesscommunications device by selectively deactivating the GPS chip whenradiofrequency signal traces suggest that the device is stationary andby only reactivating the GPS chip when the radiofrequency signal tracessuggest that the device is moving.

Thus, an aspect of the present technology is a method of controllingoperation of a Global Positioning System (GPS) receiver in a wirelesscommunications device. The method entails receiving radiofrequencysignal traces from one or more base station towers, applying a pluralityof tiered classifiers to determine from the signal traces whether thewireless communications device is moving or stationary, deactivating theGPS receiver when the wireless communications device is determined to bestationary, and reactivating the GPS receiver when the wirelesscommunications device is determined to be moving.

Another aspect of the present technology is a machine readable mediumcomprising code adapted to perform the foregoing method when themachine-readable medium is loaded into memory and executed on aprocessor of a wireless communications device.

Yet another aspect of the present technology is a wirelesscommunications device having a GPS receiver for determining a currentlocation of the device, a radiofrequency transceiver for receivingsignal traces from one or more nearby base stations, and a processoroperatively coupled to memory for applying tiered classifiers fordetermining from the signal traces whether the wireless communicationsdevice is moving or stationary, the processor deactivating the GPSreceiver when the device is determined to be stationary and reactivatingthe GPS receiver when the device is determined to be moving.

Yet a further aspect of the present technology is a method of trainingclassifiers for determining whether a wireless communications device ismoving or stationary based on radiofrequency signal traces received fromnearby base stations. The method entails identifying a plurality ofsignal features for analyzing signal traces, receiving radiofrequencysignal traces from one or more base station towers, receivingaccelerometer readings indicating whether the device is moving orstationary, and training classifiers by determining coefficients oflogistic functions.

The details and particulars of these aspects of the technology will nowbe described below, by way of example, with reference to the attacheddrawings.

FIG. 1 is a schematic depiction of a wireless communications device 100on which the present technology can be implemented. For the purposes ofthis specification, a GPS-enabled wireless communications deviceincludes a GPS-enabled smartphone, a GPS-enabled cell phone, aGPS-enabled wireless PDA, a GPS-enabled wireless computing tablet, aGPS-enabled wireless laptop, and other equivalent devices.

As shown by way of example in FIG. 1, the wireless communications device100 includes a microprocessor (referred to herein simply as a“processor”) 110 operatively coupled to memory (Flash Memory 120 and RAM130). A SIM (Subscriber Identity Module) card 112 may be provided forGSM (Global System for Mobile) devices. The wireless communicationsdevice 100 has a user interface 140 which includes a display (e.g. a LCDscreen) 150, a keyboard/keypad 155. The wireless communications devicemay also include, as depicted in this figure, a thumbwheel/trackball160. Alternatively, the user interface may include a touch screen. Thewireless communications device 100 also includes a radiofrequency (RF)transceiver chip 170 and antenna 172 for sending and receiving data overthe air, e.g. via cellular network, via satellite link, etc. Thetransceiver 170 communicates with a base station 50 (or “base stationtower”). The device may include an accelerometer 175 for training theclassifiers, as will be elaborated below.

As further illustrated in FIG. 1, the wireless communications device 100includes a microphone 180 and speaker 182 for voice communications.

As further depicted in FIG. 1, the device 100 includes a GlobalPositioning System (GPS) chipset 190 (or other equivalent positioningsubsystem) to determine the current location of the device based onreceived signals from orbiting GPS satellites 192. The GPS chipsetenables the device to be used for navigation or other location-basedservices.

Although the present disclosure refers expressly to the “GlobalPositioning System”, it should be understood that this term and itsabbreviation “GPS” are being used expansively to include anysatellite-based navigation-signal broadcast system, and would thereforeinclude other systems used around the world including the Beidou(COMPASS) system being developed by China, the multi-national Galileosystem being developed by the European Union, in collaboration withChina, Israel, India, Morocco, Saudi Arabia and South Korea, Russia'sGLONASS system, India's proposed Regional Navigational Satellite System(IRNSS), and Japan's proposed QZSS regional system.

Another sort of positioning subsystem may be used as well, e.g. aradiolocation subsystem that determines its current location usingradiolocation techniques, as will be elaborated below. In other words,the location of the device can be determined using triangulation ofsignals from in-range base towers, such as used for Wireless E911.Wireless Enhanced 911 services enable a cell phone or other wirelessdevice to be located geographically using radiolocation techniques suchas (i) angle of arrival (AOA) which entails locating the caller at thepoint where signals from two towers intersect; (ii) time difference ofarrival (TDOA), which uses multilateration like GPS, except that thenetworks determine the time difference and therefore the distance fromeach tower; and (iii) location signature, which uses “fingerprinting” tostore and recall patterns (such as multipath) which mobile phone signalsexhibit at different locations in each cell. Radiolocation techniquesmay also be used in conjunction with GPS in a hybrid positioning system.References herein to “GPS” are meant to include Assisted GPS and AidedGPS.

FIG. 2 is a schematic depiction of a wireless communications device 100in which a GPS visual indicator 195 indicates that the GPS chip isoperating. FIG. 3 is a similar depiction in which the GPS visualindicator 195 indicates that the GPS chip is inactive. These visualindicators can be any suitable text, symbol or icons. In certainembodiments, the GPS visual indicator may simply be absent, i.e. thisindicator, while useful, is not necessarily presented onscreen. A userselection option may also be provided to enable the user to configurethe device to either show this indicator or not, or to specify in whichinstances this indicator is to be displayed onscreen.

Overview

The present technology controls operation of a GPS chip by detectingwhether the wireless communications device is moving or stationary. Thisdetermination is made by analyzing radiofrequency signals received fromnearby base station towers (as shown schematically in FIG. 4). If thewireless communications device is determined to be stationary, thedevice selectively disables the GPS chip. If the wireless communicationsdevice is determined to be moving, then the GPS chip is either keptactive or, if the GPS chip has been previously deactivated, it isreactivated. The ability to classify the received signals depends on acareful initial training (or calibration) of one or more suitableclassifiers. In other words, this initial training first requires thatclassifiers be trained (calibrated) to recognize, based onradiofrequency signals received from nearby base station towers, whetherthe wireless communications device is moving or stationary.

Training the Classifiers

In order to utilize the novel technology described herein, theclassifiers must first be trained (calibrated) to recognize whether thereceived RF signals mean that the device is moving or stationary. First,data is collected using a wireless communications device equipped withan accelerometer 175. Data collected to date indicates that the deviceis, on average, stationary approximately 90% of the time. This suggeststhat battery life may be prolonged significantly by selectivelydisabling (turning off) the GPS receiver (GPS chip) for much of the timewhen it is not needed. As will be elaborated below, the determination asto whether the device is moving or stationary is made based onradiofrequency signals (e.g. GSM signals) received from one or morenearby base stations (cell towers).

This training technique represents an improvement on the techniquedescribed by Timothy Sohn et al. in a publication entitled “MobilityDetection Using Everyday GSM Traces” UbiComp 2006: Ubiquitous Computing(2006), pp. 212-224. Whereas Sohn et al. used human input to collectdata as to whether the device was actually moving or not, the improvedtechnique disclosed herein uses an accelerometer inside the device.Alternatively, the accelerometer may be externally connected butportable with the device to still provide useful readings. Furthermore,whereas Sohn et al. collected data at 1-second intervals, the improvedtechnique disclosed herein gathers data at 10-second intervals. Use of a10-second interval is believed to be more optimally suited forsubsequent implementation on a wireless communications device. Bygathering data at 10-second intervals, the device only needs to get theGSM signals and perform the calculations on that data every 10 seconds.This has less of an impact on battery and processor resources than ifthe determinations were made every single second. Since a classifier canonly properly classify new data that resembles the data with which itwas trained, the training interval (data collection interval fortraining) should be matched to the operational interval (data collectioninterval during eventual operation). Accordingly, the improved techniquecollects data every 10 seconds rather than every second.

The one or more classifiers are then developed by sampling the GSM dataat 10-second intervals, as noted above, and then performing severalcalculations on the data to produce a set of values (parameters) thatrepresent a feature space X. The feature space X is then mapped to a setof labels (“moving” or “stationary”). In other words, the feature spaceX is then plugged into a logistic function classifier. In thisembodiment, the classifier is a logistic function having coefficientsthat are determined during the training process. Once the coefficientsof the logistic function are determined, the classifier isready/operational and can be used to return a value of either “moving”or “stationary” upon receipt of new GSM data. In one specificimplementation, the classifier can be created using WEKA software. As isknown in the art, WEKA (Waikato Environment for Knowledge Analysis) ismachine-learning software written in Java. The WEKA classifier tools canbe used to determine the coefficients for the logistic function. As willbe appreciated, this novel technology can be implemented using othersoftware as well.

Signal Parameters (Signal Features)

A plurality of different signal parameters or signal features aredetermined by analyzing the radiofrequency (RF) signals received by thedevice from nearby base station towers. In general, these signals can beanalyzed in various ways to provide these parameters (“features” or“indicators”) that indicate whether the device is probably moving orprobably stationary.

In one embodiment, the feature space contains eleven (11) differentfeatures or signal parameters that can be calculated based on the signaltraces received by the wireless communications device. These are:

1) common cell towers (i.e. common base stations);

2) Spearman rank correlation coefficient;

3) Euclidean distance (with one dimension per tower);

4) Mean of Euclidean distances;

5) Euclidean distance variance;

6) Euclidean distance between endpoints (e.g. between the first andseventh data points);

7) Signal strength variance for all base station towers seen in thatwindow;

8) Mean of Euclidean distances but performed over a full 5 minutes and 1second for 31 data points (i.e. every 10 seconds over 5 minutes);

9) Euclidean distance variance but performed over a full 5 minutes and 1second for 31 data points (i.e. every 10 seconds over 5 minutes);

10) Euclidean distance between endpoints but performed over a full 5minutes and 1 second for 31 data points (i.e. every 10 seconds over 5minutes); and

11) Signal strength variance for all base station towers seen in thatwindow but performed over a full 5 minutes and 1 second for 31 datapoints (i.e. every 10 seconds over 5 minutes).

The Euclidean distance is the straight-line distance between two pointsin three-dimensional space.

The calculations or computations of these parameters can either beperformed directly on the device or offloaded wirelessly to a serverthat returns the results to the device (i.e. return a determination thateither the device is stationary or the device is moving.)

As will be appreciated, variations on the time intervals and thus thenumber of data points used may be envisaged. In other words, thesampling interval of 10 seconds may be varied, for example, to 9 secondsor 11 seconds or to any other suitable sampling interval. Likewise, thewindow over which the samples are collected may be varied. Furthermore,this novel technology may be refined or varied by employing furtherparameters (signal features) in addition to the eleven parametersdescribed above. It should be appreciated that the order of theseparameters may also be changed.

These parameters (features) can be used in various combinations toenable the device to infer whether the device is moving or stationary.With reference to FIG. 4, the number of base stations from which signaltraces are to be drawn in order to initiate the method can bepredetermined. For example, the device may be configured to require aminimum of two base stations before the method can be initiated. In FIG.4, solely by way of example, the wireless communications device is shownreceiving RF signals from three different stations, BT1, BT2, and BT3but not from BT4 which is out of range.

Tiered Classifiers

In one main implementation of this technology, a tiered approach isemployed rather than simply computing all eleven parameters (features).In other words, a tiered approach utilizes multiple classifiers thatyield a progressively more accurate determination as to whether thedevice is moving or stationary. Such a tiered approach enables rapidmotion determination (in those particular certain instances where thesignal data makes this readily apparent to the classifier) whileproviding progressively more accurate determinations over time for thoseinstances where more detailed analysis of the sampled signals areneeded. The tiered approach thus provides highly flexible analysis ofthe sampled signals that is potentially much quicker than the prior-arttechnique while still converging to an accurate determination for thoseinstances where the sampled signals yield inconclusive results at theoutset.

In one specific implementation, features 1 to 3 are calculated betweenconsecutive data points while features 4 to 11 are calculated over amoving window. In one particular implementation, features 4 to 7 arecalculated over a short window (e.g. a 1 minute 1 second window of timerepresenting 7 data points) while features 8 to 11 are calculated over along window (e.g. a 5 minute 1 second window representing 31 datapoints).

This particular implementation therefore involves using at least threeclassifiers in a tiered approach. As presented in Table 1 below,classifier #1 computes three (3) features based on only two (2)consecutive data points collected every ten (10) seconds during thefirst minute. Even though this provides a highly granular result, insome instances, it is sufficient to provide an indication that thedevice is either moving or stationary. In this particularimplementation, a second tier classifier kicks in after a minute bycomputing seven (7) features based on seven (7) data points (i.e. usinga short moving window). After five minutes, a third tier classifierbegins to compute all eleven (11) features based on thirty-one (31) datapoints (i.e. using a long moving window).

TABLE 1 Tiered Classifiers CLASSIFIER #1 first 3 2 data t = 0 to t = 59sec. features points CLASSIFIER #2 first 7 7 data t = 60 sec to t =features points 4 min 59 sec CLASSIFIER #3 all 11 31 data t = 5 minonward features points

Three tiers (or levels) of classifiers are thus employed forprogressively converging toward an increasingly more accuratedetermination as to whether the wireless device is in motion or whetherit is stationary. The primary advantage of this novel tiered approach isthat, depending on the particular signal traces being analyzed, thetiered approach can be much quicker than the approach proposed by Sohnet al. of collecting data for a full five minutes before computing theparameters. In other words, if the signal traces are unambiguouslyindicative of motion (or, conversely, unambiguously suggest that thedevice is stationary), then a determination can be made within the firstminute. If the first tier analysis is inconclusive, the second tier maybe able to provide a determination. If the second tier provides adetermination as to whether the device is moving or stationary, thenthis too would be quicker than what would have been achieved using theprior-art technique. If the second tier classifier fails to resolvewhether the device is moving or not, the third tier classifier is thencalled upon for a detailed analysis. The third tier classifier beginscomputing all eleven features as would have been done by the prior-arttechnique after the same of period of five minutes has elapsed. Thus,the tiered classification is not any slower than the prior-arttechnique. However, in certain cases, it will provide useful resultsfaster than the prior-art approach.

Optionally, a fourth classifier may be initiated at any point after thefirst five minutes have elapsed since start-up. This fourth classifier,in one specific embodiment, is trained on data collected at 60-secondintervals (rather than on data collected at 10-second intervals). Atthat point, the device can sample the RF signal traces (e.g. GSM signaltraces) every 60 seconds instead of every 10 seconds, thus furtherdiminishing the utilization of both processor and battery resources.

Optionally, a fifth classifier may be initiated after 30 minutes haveelapsed since start-up. This fifth classifier is even more accurate thanthe fourth classifier, and can be used on data collected at a differentsampling interval than what is used for the fourth classifier.

Thresholds

In order to preclude the device was vacillating (“flip-flopping”)between on and off in cases where the classifiers provide rapidlyalternating results of moving and stationary, the device may utilizethreshold functions. The threshold functions ensure that there apredetermined minimum number of consistent readings (determinations)before deactivating or reactivating the GPS chip. For example, thedevice may be configured or programmed to wait until three consecutive(i.e. consistent) readings indicating that the device is in motion areobtained before reactivating the GPS chipset. In other words, thetechnique may involve deferring reactivation of the GPS receiver untilthe device has determined for a predetermined minimum consecutive numberof times that the device is moving.

Also by way of example, the device may be programmed to wait until therehas been five minutes without motion before powering down (deactivating)the GPS chipset. In other words, the technique may involve deferringdeactivation of the GPS receiver until the device has determined thatthe device has remained stationary for a period of exceeding apredetermined time threshold. The thresholds can be varied, either bythe manufacturer, by the system administrator or by the user. Forexample, the user may prefer that the device wait ten minutes beforepowering off the GPS chip. In a variant, the device may automaticallyadjust this threshold based on the detected battery life. For example,as the battery is progressively depleted, the threshold may be adjusteddownwardly so that the GPS is shut off earlier to thus further prolongbattery life.

FIG. 5 is a flowchart depicting the main steps of a method ofcontrolling operation of a GPS chip by determining whether the devicewithin which the GPS chip is embedded is moving or stationary.

As depicted in FIG. 5, an initial step 200 is to receive radiofrequencysignal traces, e.g. GSM signal traces, from one or more nearby basestations (as was illustrated schematically in FIG. 4). A subsequent step210 of this novel method is to compute three signal parameters (signalfeatures) from the signal traces. In the example implementation depictedin the flowchart of FIG. 5, this is accomplished over a period of oneminute, i.e. from t=0 to t=59 seconds. In one implementation, the firsttier classifier employs the first three (3) parameters from the list ofeleven parameters set forth above, namely (1) common cell towers; (2)Spearman rank correlation coefficient; and (3) Euclidean distance. Thesefirst three features are used to define the first tier classifier. Thisfirst tier classifier is then applied at step 220 to determineapproximately whether the device is moving or stationary, i.e. toprovide a very quick, albeit rough, estimate as to whether the device ismoving or stationary.

This first tier classifier can thus be used after a very short period oftime to begin controlling operation of the GPS chip (step 270) based onwhether the first tier classifier suggests that the device is moving ornot. Unlike the prior art which can take five minutes to make adetermination, the first tier classifier can provide, after only one (1)minute of data collection, an initial classification (moving orstationary) that enables the device to begin the process of controllingoperation of its GPS chip at step 270.

Controlling operation of the GPS chip may involve, for example,deactivating the chip, reactivating the chip, or permitting the chip tocontinue in its current state, as the case may be. In other words, basedon these first three parameters, the device can initiate early controlof the GPS chip (step 270) even if the motion determination at thisearly stage is only approximate.

Still referring to the flowchart depicted in FIG. 5, while the GPS chipis being controlled based on the first tier classifier, further data iscollected at step 230 from t=1 min to t=4 min 59 seconds in this exampleimplementation. From this additional data are computed seven (7) signalfeatures for the second tier classifier. This second tier classifierprovides a more accurate determination as to whether the device ismoving or stationary, i.e. the second tier classifier is more accuratethan the first tier classifier. This more accurate second tierclassifier is then applied at step 240 to determine whether the deviceis moving or not. This second tier classifier is then used at step 270to control the GPS chip, e.g. by deactivating the chip, reactivating thechip or permitting the chip to continue in its current state. Thissecond tier classifier thus overrides the determination may be the lessaccurate first tier classifier.

In this example implementation, the second tier classifier employs seven(7) signal features as follows: (1) common base station towers; (2)Spearman rank correlation coefficient; (3) Euclidean distance; (4) meanof Euclidean distance over 7 data points; (5) variance of Euclideandistance over 7 data points; (6) Euclidean distance between endpointsover 7 data points; and (7) signal strength variance for all basestations in window over 7 data points.

Still referring to the flowchart depicted in FIG. 5, while the GPS chipis being controlled based on the second tier classifier, yet furtherdata is collected at step 250 from t=5 minutes onward in this exampleimplementation. From t=5 minutes onward, the third tier classifier(which is more accurate than the first and second classifiers) iscomputed using all eleven (11) signal features. This third tierclassifier is then applied at step 260 to determine if the device ismoving or stationary. This third tier classifier is thus used to control(e.g. deactivate or reactivate) the GPS chip at step 270 based onwhether the third tier classifier suggests that the device is moving orstationary.

In this example implementation, the third tier classifier employs alleleven (11) signal features as follows: (1) common base station towers;(2) Spearman rank correlation coefficient; (3) Euclidean distance; (4)mean of Euclidean distance over 7 data points; (5) variance of Euclideandistance over 7 data points; (6) Euclidean distance between endpointsover 7 data points; (7) signal strength variance for all base stationsin window over 7 data points; (8) Mean of Euclidean distances butperformed over a full 5 minutes and 1 second for 31 data points (i.e.every 10 seconds over 5 minutes); (9) Euclidean distance variance butperformed over a full 5 minutes and 1 second for 31 data points (i.e.every 10 seconds over 5 minutes); (10) Euclidean distance betweenendpoints but performed over a full 5 minutes and 1 second for 31 datapoints (i.e. every 10 seconds over 5 minutes); and (11) Signal strengthvariance for all base station towers seen in that window but performedover a full 5 minutes and 1 second for 31 data points (i.e. every 10seconds over 5 minutes).

In another implementation, fourth, fifth or any number of subsequenttiered classifiers can be utilized to provide additional tiers. Theseadditional tiers may, for example, collect data samples at a longer timeinterval. For example, a fourth tier classifier could be created forsampling signal traces and computing signal features every 60 seconds.This could be used to reduce the computational burden on the device oncethe third tier classifier has been applied. A fifth (or subsequent) tierclassifier may be defined having a different sampling interval than thefourth classifier, for example.

From the foregoing, it will be understood that the classifiers eachcomprise sets or groups of calculations or computations of distance,variance, mean, etc. that are performed on the signal traces. For anygiven classifier, these calculations or computations may be performedsequentially or in parallel. The result of each such calculationrepresents a particular feature of the signal or signal parameter. Thesefeatures or parameters are then grouped together to provide aclassification result for each classifier. The classifier thusclassifies the signal traces as indicative of motion or a lack thereof.

FIG. 6 is a flowchart depicting main steps of a method of training theclassifiers, as was described above. This method of training classifiersinvolves a step 300 of identifying a plurality of signal features foranalyzing signal traces, a step 310 of receiving radiofrequency signaltraces from one or more base station towers, a step 320 of receivingaccelerometer readings indicating whether the device is moving orstationary, and a step 330 of training classifiers by determiningcoefficients of logistic functions.

The determination as to whether the device is moving or stationary isused to control operation of the GPS chip (also referred to herein asthe GPS receiver or GPS chipset) or any other positioning subsystem orlocation-determining hardware, software or firmware on the device. Amanual override may be provided to enable the user to reactivate the GPSchip (when the device is stationary) or, conversely, to shut it off(when the device is moving). A user configuration menu or options pagemay be provided to enable the user to specify conditions or preferencesfor how operation of the GPS receiver is to be controlled. For example,the options page may have settings to enable the operation of the GPSreceiver to be control based on time parameters or location parameters.For example, if the device is operating in a certain place and/or at acertain time, the device may override the classifiers to either keep theGPS on or, conversely, shut it off, despite what the classifiers wouldnormally suggest doing.

The technology disclosed herein may be implemented in hardware,software, firmware or any combination thereof. For example, theforegoing methods can be implemented as coded instructions on amachine-readable medium. In other words, the machine-readable medium maybe a computer-readable medium upon which software code is recorded toperform the foregoing method or methods when the machine-readable mediumis loaded into memory and executed on the microprocessor of the wirelesscommunications device or on any other computing device connected to thewireless communications device.

The present technology has been implemented using GSM technology. For aGSM implementation, the operating system (O/S level) passes signal traceinformation from all towers in range to the Java level where it can bereadily utilized for motion analysis. For a CDMA implementation, on theother hand, as will be appreciated by those of skill in the art, thesignal trace information is not automatically passed up to the Javalevel. Therefore, to implement this technology using CDMA, it isnecessary to bridge the O/S and Java levels to ensure that the signaltrace information is available at the Java level.

This new technology has been described in terms of specificimplementations and configurations (and variants thereof) which areintended to be exemplary only. Persons of ordinary skill in the art willtherefore appreciate that many obvious modifications, variations,refinements and alterations may be made that do not depart from theinventive concept(s) disclosed herein. The scope of the exclusive rightsought by the applicant is therefore intended to be limited solely bythe appended claims.

1. A method of controlling operation of a Global Positioning System(GPS) receiver in a wireless communications device, the methodcomprising: receiving radiofrequency signal traces from one or more basestation towers; applying a plurality of tiered classifiers to determinefrom the signal traces whether the wireless communications device ismoving or stationary; deactivating the GPS receiver when the wirelesscommunications device is determined to be stationary; and reactivatingthe GPS receiver when the wireless communications device is determinedto be moving.
 2. The method as claimed in claim 1 wherein applying theplurality of tiered classifiers comprises: applying a first tierclassifier based on three signal features; applying a second tierclassifier based on seven signal features; and applying a third tierclassifier based on eleven signal features.
 3. The method as claimed inclaim 2 wherein applying the first tier classifier comprises computingthree signal features over two consecutive data points for a firstminute.
 4. The method as claimed in claim 3 wherein applying the secondtier classifier comprises computing seven signal features over a shortmoving window of seven data points after one minute has elapsed for asubsequent four minutes.
 5. The method as claimed in claim 4 whereinapplying the third tier classifier comprises computing eleven signalfeatures over a long moving window of thirty-one data points after fiveminutes have elapsed.
 6. The method as claimed in claim 2 whereinapplying the plurality of tiered classifiers further comprises applyinga fourth tier classifier by sampling signal traces and computing signalfeatures every 60 seconds.
 7. The method as claimed in claim 6 whereinapplying the plurality of tiered classifiers further comprises applyinga fifth tier classifier having a sampling interval different from asampling interval of the fourth tier classifier.
 8. The method asclaimed in claim 2 wherein the three signal features used for the firsttier classifier are: (i) common base station towers; (ii) Spearman rankcorrelation coefficient; and (iii) Euclidean distance.
 9. The method asclaimed in claim 2 wherein the seven signal features used for the secondtier classifier are: (i) common base station towers; (ii) Spearman rankcorrelation coefficient; (iii) Euclidean distance; (iv) mean ofEuclidean distance over 7 data points; (v) variance of Euclideandistance over 7 data points; (vi) Euclidean distance between endpointsover 7 data points; and (vii) signal strength variance for all basestations in window over 7 data points.
 10. The method as claimed inclaim 2 wherein the eleven signal features used for the third tierclassifier are: (i) common base station towers; (ii) Spearman rankcorrelation coefficient; (iii) Euclidean distance; (iv) mean ofEuclidean distance over 7 data points; (v) variance of Euclideandistance over 7 data points; (vi) Euclidean distance between endpointsover 7 data points; (vii) signal strength variance for all base stationsin window over 7 data points; (viii) mean of Euclidean distance over 31data points; (ix) variance of Euclidean distance over 31 data points;(x) Euclidean distance between endpoints over 31 data points; and (xi)signal strength variance for all base stations in window over 31 datapoints.
 11. The method as claimed in claim 1 further comprisingdeferring reactivation of the GPS receiver until the device hasdetermined for a predetermined minimum consecutive number of times thatthe device is moving.
 12. The method as claimed in claim 1 furthercomprising deferring deactivation of the GPS receiver until the devicehas determined that the device has remained stationary for a period ofexceeding a predetermined time threshold.
 13. A machine-readable storagemedium comprising instructions in code which, when loaded into memoryand executed on a processor of a wireless communications device,controls operation of a Global Positioning System receiver in thewireless communications device by: receiving radiofrequency signaltraces from one or more base station towers; applying a plurality oftiered classifiers to determine from the signal traces whether thewireless communications device is moving or stationary; deactivating theGPS receiver when the wireless communications device is determined to bestationary; and reactivating the GPS receiver when the wirelesscommunications device is determined to be moving.
 14. A wirelesscommunications device comprising: a GPS receiver for determining acurrent location of the device; a radiofrequency transceiver forreceiving signal traces from one or more nearby base stations; and aprocessor operatively coupled to memory for applying tiered classifiersfor determining from the signal traces whether the wirelesscommunications device is moving or stationary, the processordeactivating the GPS receiver when the device is determined to bestationary and reactivating the GPS receiver when the device isdetermined to be moving.
 15. The device as claimed in claim 14 whereinthe processor applies a first tier classifier based on three signalfeatures, a second tier classifier based on seven signal features, and athird tier classifier based on eleven signal features.
 16. The device asclaimed in claim 15 wherein the first tier classifier computes threesignal features over two consecutive data points for a first minute. 17.The device as claimed in claim 16 wherein the second tier classifiercomputes seven signal features over a short moving window of seven datapoints after one minute has elapsed for a subsequent four minutes. 18.The device as claimed in claim 17 wherein the third tier classifiercomputes eleven signal features over a long moving window of thirty-onedata points after five minutes have elapsed.
 19. The device as claimedin claim 16 wherein the three signal features used for the first tierclassifier are: (i) common base station towers; (ii) Spearman rankcorrelation coefficient; and (iii) Euclidean distance.
 20. The device asclaimed in claim 17 wherein the seven signal features used for thesecond tier classifier are: (i) common base station towers; (ii)Spearman rank correlation coefficient; (iii) Euclidean distance; (iv)mean of Euclidean distance over 7 data points; (v) variance of Euclideandistance over 7 data points; (vi) Euclidean distance between endpointsover 7 data points; and (vii) signal strength variance for all basestations in window over 7 data points.
 21. The device as claimed inclaim 18 wherein the eleven signal features used for the third tierclassifier are: (i) common base station towers; (ii) Spearman rankcorrelation coefficient; (iii) Euclidean distance; (iv) mean ofEuclidean distance over 7 data points; (v) variance of Euclideandistance over 7 data points; (vi) Euclidean distance between endpointsover 7 data points; (vii) signal strength variance for all base stationsin window over 7 data points; (viii) mean of Euclidean distance over 31data points; (ix) variance of Euclidean distance over 31 data points;(x) Euclidean distance between endpoints over 31 data points; and (xi)signal strength variance for all base stations in window over 31 datapoints.
 22. A method of training classifiers for determining whether awireless communications device is moving or stationary based onradiofrequency signal traces received from nearby base stations, themethod comprising: identifying a plurality of signal features foranalyzing signal traces; receiving radiofrequency signal traces from oneor more base station towers; receiving accelerometer readings indicatingwhether the device is moving or stationary; and training classifiers bydetermining coefficients of logistic functions.
 23. The method asclaimed in claim 22 wherein training the classifiers comprises traininga first tier classifier, a second tier classifier and a third tierclassifier that provide a progressively more accurate determination asto whether the wireless communications device is moving or stationary.